Scattering and attenuation of light in no-homogeneous imaging media or
inconsistent light intensity will cause insufficient contrast and color
distortion in the collected images, which limits the developments such as
vision-driven smart urban, autonomous vehicles, and intelligent robots. In this
paper, we propose an all-in-one scene recovery network via multi-knowledge
integration (termed AoSRNet) to improve the visibility of imaging devices in
typical low-visibility imaging scenes (e.g., haze, sand dust, and low light).
It combines gamma correction (GC) and optimized linear stretching (OLS) to
create the detail enhancement module (DEM) and color restoration module (CRM).
Additionally, we suggest a multi-receptive field extraction module (MEM) to
attenuate the loss of image texture details caused by GC nonlinear and OLS
linear transformations. Finally, we refine the coarse features generated by
DEM, CRM, and MEM through Encoder-Decoder to generate the final restored image.
Comprehensive experimental results demonstrate the effectiveness and stability
of AoSRNet compared to other state-of-the-art methods. The source code is
available at https://github.com/LouisYuxuLu/AoSRNet.

通过多知识整合提出了一种名为 AoSRNet 的多合一场景恢复网络，以改善在低可见性成像场景中的成像设备的可视性，并综合最新方法进行了充分的实验验证。

AoSRNet：通过多知识整合的全能场景恢复网络

AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge  Integration

Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in
widespread use in low- and middle-income countries (LMICs) and are commonly
used for some applications in higher income countries e.g. for small child
patients with obesity, claustrophobia, implants, or tattoos. However, low-field
MR images commonly have lower resolution and poorer contrast than images from
high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT)
to enhance low-field structural MRI by estimating from a low-field image the
image we would have obtained from the same subject at high field. Our approach
uses (i) a stochastic low-field image simulator as the forward model to capture
uncertainty and variation in the contrast of low-field images corresponding to
a particular high-field image, and (ii) an anisotropic U-Net variant
specifically designed for the IQT inverse problem. We evaluate the proposed
algorithm both in simulation and using multi-contrast (T1-weighted,
T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical
low-field MRI data from an LMIC hospital. We show the efficacy of IQT in
improving contrast and resolution of low-field MR images. We demonstrate that
IQT-enhanced images have potential for enhancing visualisation of anatomical
structures and pathological lesions of clinical relevance from the perspective
of radiologists. IQT is proved to have capability of boosting the diagnostic
value of low-field MRI, especially in low-resource settings.

本研究提出了一种名为影像质量转移（IQT）的方法，通过建立低场成像与高场成像之间的关联来增强低场结构性 MRI 图像的对比度和分辨率。结果表明，IQT 增强的图像有望提高低资源环境下低场 MRI 的诊断效果。

基于随机图像质量转移的低场磁共振图像增强

Low-field magnetic resonance image enhancement via stochastic image  quality transfer

Improvement guarantees for semi-supervised classifiers can currently only be
given under restrictive conditions on the data. We propose a general way to
perform semi-supervised parameter estimation for likelihood-based classifiers
for which, on the full training set, the estimates are never worse than the
supervised solution in terms of the log-likelihood. We argue, moreover, that we
may expect these solutions to really improve upon the supervised classifier in
particular cases. In a worked-out example for LDA, we take it one step further
and essentially prove that its semi-supervised version is strictly better than
its supervised counterpart. The two new concepts that form the core of our
estimation principle are contrast and pessimism. The former refers to the fact
that our objective function takes the supervised estimates into account,
enabling the semi-supervised solution to explicitly control the potential
improvements over this estimate. The latter refers to the fact that our
estimates are conservative and therefore resilient to whatever form the true
labeling of the unlabeled data takes on. Experiments demonstrate the
improvements in terms of both the log-likelihood and the classification error
rate on independent test sets.

提出了一种通用的半监督参数估计方法，通过对 “对比度” 和 “悲观主义” 等概念的引入，实现了半监督分类器的改进，并在 LDA 的案例研究中证明了该方法的有效性。